Journal of Data Science logo


Login Register

  1. Home
  2. Issues
  3. Volume 15, Issue 3 (2017)
  4. The Generalized Marshall-Olkin-Kumaraswa ...

Journal of Data Science

Submit your article Information
  • Article info
  • More
    Article info

The Generalized Marshall-Olkin-Kumaraswamy-G Family of Distributions
Volume 15, Issue 3 (2017), pp. 391–422
Subrata Chakraborty   Laba Handique  

Authors

 
Placeholder
https://doi.org/10.6339/JDS.201707_15(3).0003
Pub. online: 4 August 2022      Type: Research Article      Open accessOpen Access

Published
4 August 2022

Abstract

Abstract: A family of distribution is proposed by using Kumaraswamy-G ( Kw − G ) distribution as the base line distribution in the generalized Marshall-Olkin (GMO) construction. By expanding the probability density function and the survival function as infinite series the proposed family is seen as infinite mixtures of the Kw − G distribution. Series expansions of the density function for order statistics are also obtained. Moments, moment generating function, Rényi entropy, quantile function, random sample generation, asymptotes, shapes and stochastic orderings are also investigated. Maximum likelihood estimation, their large sample standard error, confidence intervals and method of moment are presented. Three real life illustrations of comparative data modeling applications with some of the important sub mode

PDF XML
PDF XML

Copyright
No copyright data available.

Keywords
Marshall - Olkin -Kumaraswamy-G family Generalized Marshall-Olkin family Exponentiated family

Metrics
since February 2021
786

Article info
views

397

PDF
downloads

Export citation

Copy and paste formatted citation
Placeholder

Download citation in file


Share


RSS

Journal of data science

  • Online ISSN: 1683-8602
  • Print ISSN: 1680-743X

About

  • About journal

For contributors

  • Submit
  • OA Policy
  • Become a Peer-reviewer

Contact us

  • JDS@ruc.edu.cn
  • No. 59 Zhongguancun Street, Haidian District Beijing, 100872, P.R. China
Powered by PubliMill  •  Privacy policy